Æ·±åº¦ä¿¡ç”¨é£Žé™© (Deep Credit Risk) - Ľ¿ç”¨python进行机器学习

- 了解æµåŠ¨æ€§ï¼Œæˆ¿å±‹å‡€å€¼å’Œè®¸å¤šå…¶ä»–关键银行业特å¾å˜é‡çš„作用；

- 选择并处ç†å˜é‡ï¼›

- 预测è¿çº¦ã€å¿ä»˜ã€æŸå¤±çŽ‡å’Œé£Žé™©æ•žå£ï¼›

- 利用å±æœºå‰ç‰¹å¾é¢„测ç»æµŽè¡°é€€å’Œå±æœºåŽæžœï¼›

- ç†è§£COVID-19对信用风险带æ¥çš„å½±å“ï¼›

- 将创新的抽样技术应用于模型训练和验è¯ï¼›

- 从Logit分类器到éšæœºæ£®æž—和神ç»ç½‘络的深入学习;

- 进行无监ç£èšç±»ã€ä¸»æˆåˆ†å’Œè´å¶æ–¯æŠ€æœ¯çš„应用；

- 为CECLã€IFRS 9å’ŒCCAR建立多周期模型；

- 建立用于在险价值和期望æŸå¤±çš„信贷组åˆç›¸å…³æ¨¡åž‹ï¼›

- 使用更多真实的信用风险数æ®å¹¶è¿è¡Œè¶…过1500行的代ç ...




- Understand the role of liquidity, equity and many other key banking features

- Engineer and select features

- Predict defaults, payoffs, loss rates and exposures

- Predict downturn and crisis outcomes using pre-crisis features

- Understand the implications of COVID-19

- Apply innovative sampling techniques for model training and validation

- Deep-learn from Logit Classifiers to Random Forests and Neural Networks

- Do unsupervised Clustering, Principal Components and Bayesian Techniques

- Build multi-period models for CECL, IFRS 9 and CCAR

- Build credit portfolio correlation models for VaR and Expected Shortfal

- Run over 1,500 lines of pandas, statsmodels and scikit-learn Python code

- Access real credit data and much more ...